Abstract: The dynamic time warping is a classic similarity measure which can handle time warping issue in similarity computation of time series, and the DTW with constrained warping window is the most common and practical form of DTW. After systematically analyzing the traditional learning method for optimal warping window of D"I}W, we introduced time distance to measure the time deviation between two time series,and proposed a new leaning method for optimal warping window based on time distance. Since the time distance is an appurtenant of the DTW computation, the new method can improve D"TW classification accuracy with little additional computation. Experimental data show that the optimal DTW with best warping window gets better classification accuracy when the new learning method is employed.What is more,the classification accuracy is better than the ERP(Edit Distance with Rcal Penalty) and the LCSS(Longest Common SubSequcnce) , and is close to the TWED(Time Warp Edit Distance).

Key words: Time series,Similarity measure,Dynamic time warping,Warping path,Time distance